Contents: Ensemble Techniques Project

1. Part-A: Solution
2. Part-B: Solution

Part-A: Solution

• DOMAIN: Telecom
• CONTEXT: A telecom company wants to use their historical customer data to predict behaviour to retain customers. You can analyse all relevant customer data and develop focused customer retention programs.
• DATA DESCRIPTION: Each row represents a customer, each column contains customer’s attributes described on the column Metadata. The data set includes information about:
• Customers who left within the last month – the column is called Churn
• Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
• Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
• Demographic info about customers – gender, age range, and if they have partners and dependents
• PROJECT OBJECTIVE: To Build a model that will help to identify the potential customers who have a higher probability to churn. This helps the company to understand the pinpoints and patterns of customer churn and will increase the focus on strategizing customer retention.

# Import all the relevant libraries needed to complete the analysis, visualization, modeling and presentation
import pandas as pd
import numpy as np
import os

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set_style('darkgrid')
%matplotlib inline

from scipy import stats
from scipy.stats import zscore

from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import RobustScaler
from sklearn import model_selection
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc, roc_auc_score

from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
import xgboost as xgb
from xgboost import plot_importance

from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTENC
from imblearn.over_sampling import SMOTE

import warnings
warnings.filterwarnings("ignore")

C:\Anaconda\lib\site-packages\xgboost\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
  from pandas import MultiIndex, Int64Index

1. Data Understanding and Exploration:

1A. Read ‘TelcomCustomer-Churn_1.csv’ as a DataFrame and assign it to a variable.

# CSV File 1
dfa1=pd.read_csv('TelcomCustomer-Churn_1.csv')

dfa1.info()
dfa1.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 10 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   customerID       7043 non-null   object
 1   gender           7043 non-null   object
 2   SeniorCitizen    7043 non-null   int64 
 3   Partner          7043 non-null   object
 4   Dependents       7043 non-null   object
 5   tenure           7043 non-null   int64 
 6   PhoneService     7043 non-null   object
 7   MultipleLines    7043 non-null   object
 8   InternetService  7043 non-null   object
 9   OnlineSecurity   7043 non-null   object
dtypes: int64(2), object(8)
memory usage: 550.4+ KB

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No

1B. Read ‘TelcomCustomer-Churn_2.csv’ as a DataFrame and assign it to a variable.

# CSV File 2
dfa2=pd.read_csv('TelcomCustomer-Churn_2.csv')

dfa2.info()
dfa2.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 12 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   OnlineBackup      7043 non-null   object 
 2   DeviceProtection  7043 non-null   object 
 3   TechSupport       7043 non-null   object 
 4   StreamingTV       7043 non-null   object 
 5   StreamingMovies   7043 non-null   object 
 6   Contract          7043 non-null   object 
 7   PaperlessBilling  7043 non-null   object 
 8   PaymentMethod     7043 non-null   object 
 9   MonthlyCharges    7043 non-null   float64
 10  TotalCharges      7043 non-null   object 
 11  Churn             7043 non-null   object 
dtypes: float64(1), object(11)
memory usage: 660.4+ KB

	customerID	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

1C. Merge both the DataFrames on key ‘customerID’ to form a single DataFrame

# customerID is common in both the dataframes
dfa=dfa1.merge(dfa2, left_on='customerID', right_on='customerID')

dfa.info()
dfa.head()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.2+ MB

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	...	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

5 rows × 21 columns

1D. Verify if all the columns are incorporated in the merged DataFrame by using simple comparison Operator in Python.

# By default, pd.merge gives you an inner merge so you only see the matched.
# Approach 1 is to do instead an outer merge
# And check which rows have np.nan for identifying those that could have missed out from an inner merge:

option1 = dfa1.merge(dfa2, on='customerID', how='outer')

print(option1[option1.isna().any(axis=1)])

Empty DataFrame
Columns: [customerID, gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges, Churn]
Index: []

[0 rows x 21 columns]

dfa.columns

Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],
      dtype='object')

2. Data Cleaning & Analysis:

2A. Impute missing/unexpected values in the DataFrame.

(This part will continue in 2B)

# Percentage of missing values

# df.isnull().sum()
# df.isna().sum()

def missing_check(df):
    total = df.isnull().sum().sort_values(ascending=False)   # total number of null values
    percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)  # percentage of values that are null
    missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])  # putting the above two together
    return missing_data # return the dataframe
missing_check(dfa)

	Total	Percent
customerID	0	0.0
DeviceProtection	0	0.0
TotalCharges	0	0.0
MonthlyCharges	0	0.0
PaymentMethod	0	0.0
PaperlessBilling	0	0.0
Contract	0	0.0
StreamingMovies	0	0.0
StreamingTV	0	0.0
TechSupport	0	0.0
OnlineBackup	0	0.0
gender	0	0.0
OnlineSecurity	0	0.0
InternetService	0	0.0
MultipleLines	0	0.0
PhoneService	0	0.0
tenure	0	0.0
Dependents	0	0.0
Partner	0	0.0
SeniorCitizen	0	0.0
Churn	0	0.0

# Get a list of categories of categorical variable
print(dfa.gender.value_counts())
print(dfa.SeniorCitizen.value_counts())
print(dfa.Partner.value_counts())
print(dfa.Dependents.value_counts())
print(dfa.tenure.value_counts())
print(dfa.PhoneService.value_counts())
print(dfa.MultipleLines.value_counts())
print(dfa.InternetService.value_counts())
print(dfa.OnlineSecurity.value_counts())
print(dfa.OnlineBackup.value_counts())
print(dfa.DeviceProtection.value_counts())
print(dfa.TechSupport.value_counts())
print(dfa.StreamingTV.value_counts())
print(dfa.StreamingMovies.value_counts())
print(dfa.Contract.value_counts())
print(dfa.PaperlessBilling.value_counts())
print(dfa.PaymentMethod.value_counts())
print(dfa.MonthlyCharges.value_counts())
print(dfa.TotalCharges.value_counts())
print(dfa.Churn.value_counts())

Male      3555
Female    3488
Name: gender, dtype: int64
0    5901
1    1142
Name: SeniorCitizen, dtype: int64
No     3641
Yes    3402
Name: Partner, dtype: int64
No     4933
Yes    2110
Name: Dependents, dtype: int64
1     613
72    362
2     238
3     200
4     176
     ... 
28     57
39     56
44     51
36     50
0      11
Name: tenure, Length: 73, dtype: int64
Yes    6361
No      682
Name: PhoneService, dtype: int64
No                  3390
Yes                 2971
No phone service     682
Name: MultipleLines, dtype: int64
Fiber optic    3096
DSL            2421
No             1526
Name: InternetService, dtype: int64
No                     3498
Yes                    2019
No internet service    1526
Name: OnlineSecurity, dtype: int64
No                     3088
Yes                    2429
No internet service    1526
Name: OnlineBackup, dtype: int64
No                     3095
Yes                    2422
No internet service    1526
Name: DeviceProtection, dtype: int64
No                     3473
Yes                    2044
No internet service    1526
Name: TechSupport, dtype: int64
No                     2810
Yes                    2707
No internet service    1526
Name: StreamingTV, dtype: int64
No                     2785
Yes                    2732
No internet service    1526
Name: StreamingMovies, dtype: int64
Month-to-month    3875
Two year          1695
One year          1473
Name: Contract, dtype: int64
Yes    4171
No     2872
Name: PaperlessBilling, dtype: int64
Electronic check             2365
Mailed check                 1612
Bank transfer (automatic)    1544
Credit card (automatic)      1522
Name: PaymentMethod, dtype: int64
20.05     61
19.85     45
19.95     44
19.90     44
20.00     43
          ..
23.65      1
114.70     1
43.65      1
87.80      1
78.70      1
Name: MonthlyCharges, Length: 1585, dtype: int64
          11
20.2      11
19.75      9
20.05      8
19.9       8
          ..
6849.4     1
692.35     1
130.15     1
3211.9     1
6844.5     1
Name: TotalCharges, Length: 6531, dtype: int64
No     5174
Yes    1869
Name: Churn, dtype: int64

2B. Make sure all the variables with continuous values are of ‘Float’ type.

For Example: MonthlyCharges, TotalCharges

dfa.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.2+ MB

dfa.head().T

	0	1	2	3	4
customerID	7590-VHVEG	5575-GNVDE	3668-QPYBK	7795-CFOCW	9237-HQITU
gender	Female	Male	Male	Male	Female
SeniorCitizen	0	0	0	0	0
Partner	Yes	No	No	No	No
Dependents	No	No	No	No	No
tenure	1	34	2	45	2
PhoneService	No	Yes	Yes	No	Yes
MultipleLines	No phone service	No	No	No phone service	No
InternetService	DSL	DSL	DSL	DSL	Fiber optic
OnlineSecurity	No	Yes	Yes	Yes	No
OnlineBackup	Yes	No	Yes	No	No
DeviceProtection	No	Yes	No	Yes	No
TechSupport	No	No	No	Yes	No
StreamingTV	No	No	No	No	No
StreamingMovies	No	No	No	No	No
Contract	Month-to-month	One year	Month-to-month	One year	Month-to-month
PaperlessBilling	Yes	No	Yes	No	Yes
PaymentMethod	Electronic check	Mailed check	Mailed check	Bank transfer (automatic)	Electronic check
MonthlyCharges	29.85	56.95	53.85	42.3	70.7
TotalCharges	29.85	1889.5	108.15	1840.75	151.65
Churn	No	No	Yes	No	Yes

# Change the Datatype of quantitative features
col_cat=['SeniorCitizen','tenure','MonthlyCharges','TotalCharges']
    
#Function to convert the categorical to quantitative
def convert_to_cont(feature):
    dfa[feature]=pd.to_numeric(dfa[feature], errors='coerce')
    
for c in col_cat:
    convert_to_cont(c)

dfa.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7032 non-null   float64
 20  Churn             7043 non-null   object 
dtypes: float64(2), int64(2), object(17)
memory usage: 1.2+ MB

dfa.isnull().sum()

customerID           0
gender               0
SeniorCitizen        0
Partner              0
Dependents           0
tenure               0
PhoneService         0
MultipleLines        0
InternetService      0
OnlineSecurity       0
OnlineBackup         0
DeviceProtection     0
TechSupport          0
StreamingTV          0
StreamingMovies      0
Contract             0
PaperlessBilling     0
PaymentMethod        0
MonthlyCharges       0
TotalCharges        11
Churn                0
dtype: int64

dfa.fillna(dfa.mean(),inplace = True)

dfa.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   float64
 20  Churn             7043 non-null   object 
dtypes: float64(2), int64(2), object(17)
memory usage: 1.2+ MB

# CustomerID is the id of the customer with corresponding details. This information may not be requried
# for analysis and modeling as the customerID will be all unique values. So we can drop it safely.
dfa.drop(['customerID'], axis=1, inplace=True)

dfa.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 20 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   gender            7043 non-null   object 
 1   SeniorCitizen     7043 non-null   int64  
 2   Partner           7043 non-null   object 
 3   Dependents        7043 non-null   object 
 4   tenure            7043 non-null   int64  
 5   PhoneService      7043 non-null   object 
 6   MultipleLines     7043 non-null   object 
 7   InternetService   7043 non-null   object 
 8   OnlineSecurity    7043 non-null   object 
 9   OnlineBackup      7043 non-null   object 
 10  DeviceProtection  7043 non-null   object 
 11  TechSupport       7043 non-null   object 
 12  StreamingTV       7043 non-null   object 
 13  StreamingMovies   7043 non-null   object 
 14  Contract          7043 non-null   object 
 15  PaperlessBilling  7043 non-null   object 
 16  PaymentMethod     7043 non-null   object 
 17  MonthlyCharges    7043 non-null   float64
 18  TotalCharges      7043 non-null   float64
 19  Churn             7043 non-null   object 
dtypes: float64(2), int64(2), object(16)
memory usage: 1.1+ MB

Quick EDA

# Describe function generates descriptive statistics that summarize the central tendency, 
# dispersion and shape of a dataset’s distribution, excluding NaN values.

# This method tells us a lot of things about a dataset. One important thing is that 
# the describe() method deals only with numeric values. It doesn't work with any 
# categorical values. So if there are any categorical values in a column the describe() 
# method will ignore it and display summary for the other columns.
dfa.describe().T

	count	mean	std	min	25%	50%	75%	max
SeniorCitizen	7043.0	0.162147	0.368612	0.00	0.000	0.00	0.00	1.00
tenure	7043.0	32.371149	24.559481	0.00	9.000	29.00	55.00	72.00
MonthlyCharges	7043.0	64.761692	30.090047	18.25	35.500	70.35	89.85	118.75
TotalCharges	7043.0	2283.300441	2265.000258	18.80	402.225	1400.55	3786.60	8684.80

# Distribution of Continuous Features
int_feat = dfa.select_dtypes(exclude=['object','category']).columns
fig, ax = plt.subplots(nrows=2, ncols = 2, figsize=(15,8), constrained_layout=True)
ax=ax.flatten()
for c,i in enumerate(int_feat):
    sns.histplot(dfa[i], ax=ax[c], bins=10)
    ax[c].set_title(i)

# Distribution of Categorical Features
cat_cols = dfa.select_dtypes(include=['object','category']).columns
fig, ax = plt.subplots(nrows=4, ncols=4, figsize=(15,15), constrained_layout=True)
ax=ax.flatten()
for x,i in enumerate(cat_cols):
    sns.countplot(x=dfa[i], ax=ax[x])

# Visualize a pairplot with 2 classes distinguished by colors
sns.pairplot(dfa,hue='Churn', corner=True )

<seaborn.axisgrid.PairGrid at 0x28120bcb250>

sns.heatmap(dfa.corr(), annot=True)

<AxesSubplot:>

# Box Plot
plt.figure(figsize=(20,8))
ax = sns.boxplot(data=dfa, orient="h", palette="Set2")

# Distribution of Target Variable
count_no_churn = (dfa['Churn'] == 'No').sum()
print("Number of customers who didn't churn:",count_no_churn)
count_yes_churn = (dfa['Churn']== 'Yes').sum()
print("Number of customers who churned:",count_yes_churn)

Number of customers who didn't churn: 5174
Number of customers who churned: 1869

fig, ax = plt.subplots(figsize=(20,8))
width = len(dfa['Churn'].unique())+6
fig.set_size_inches(width , 8)
ax=sns.countplot(data = dfa, x= 'Churn') 

for p in ax.patches: 
    ax.annotate(str((np.round(p.get_height()/len(dfa)*100,decimals=2)))+'%', 
    (p.get_x()+p.get_width()/2., p.get_height()), ha='center', va='center', 
    xytext=(0, 10), textcoords='offset points')

Imbalanced dataset:

• The Target variable is not equally distributed, only 26.54% of customers have Churned.
• The model may be biased towards the majority class i.e. Not Churned Customers.
• Data can be balanced using the SMOTE.

2C. Create a function that will accept a DataFrame as input and return pie-charts for all the appropriate Categorical features. Clearly show percentage distribution in the pie-chart

#Function to plot Pie-Charts for all categorical variables in the dataframe
def pie_charts_for_CategoricalVar(df,m):
    '''Takes in a dataframe(df_pie) and plots pie charts for all categorical 
    columns. m = number of columns required in grid'''
    
    #get all the column names in the dataframe
    a = []
    for i in df:
        a.append(i)
    
    #isolate the categorical variable names from a to b
    b = []
    for i in a:
        if (df[i].dtype.name) == 'object':
            b.append(i)
        
    plt.figure(figsize=(15, 12))
    plt.subplots_adjust(hspace=0.2)
    plt.suptitle("Pie-Charts for Categorical Variables in the dataframe", fontsize=18, y=0.95)
    
    # number of columns, as inputted while calling the function
    ncols = m
    # calculate number of rows
    nrows = len(b) // ncols + (len(b) % ncols > 0)
    
    # loop through the length of 'b' and keep track of index
    for n, i in enumerate(b):
        # add a new subplot iteratively using nrows and ncols
        ax = plt.subplot(nrows, ncols, n + 1)

        # filter df and plot 'i' on the new subplot axis
        df.groupby(i).size().plot(kind='pie', autopct='%.2f%%',ax=ax)
        
        ax.set_title(i.upper())
        ax.set_xlabel("")
        ax.set_ylabel("")
    plt.show()

pie_charts_for_CategoricalVar(dfa,4)

2D. Share insights for Q2.C.

Insights from the above pie charts:

1. Most Features are not having the equitable distribution of various classes: Dependents, PhoneService, MultipleLines, TechSupport, StreamingTV,StreamingMovies, Contract, PaperlessBilling, Churn Etc.
2. Very few features have a balanced or almost balanced distributions of various classes: Gender, Partner, PaymentMethod Etc.
3. There is huge imbalance in target vector i.e. Churn:

If the imbalanced data is not treated beforehand, then this will degrade the performance of the ML model. Most of the predictions will correspond to the majority class and treat the minority class of features as noise in the data and ignore them. This results in a high bias and low performance of the model.

A widely adopted technique for dealing with highly unbalanced datasets is called re-sampling.

Two widely used re-sampling methods are:

• Under-sampling: It is the process where you randomly delete some of the observations from the majority class in order to match the numbers with the minority class.

• Over-sampling: It is the process of generating synthetic data that tries to randomly generate a sample of the attributes from observations in the minority class

• Here we will use oversampling because under-sampling may remove important information from the dataset

2E. Encode all the appropriate Categorical features with the best suitable approach.

There are a lot of Yes/No values, we can replace them with 1 or 0:

• PhoneService - is the telephone service connected (Yes, No) - (1,0)
• MultipleLines - are multiple phone lines connected (Yes, No, No phone service) - (1,0,0)
• InternetService - client's Internet service provider (DSL, Fiber optic, No) -(2,1,0)
• OnlineSecurity - is the online security service connected (Yes, No, No internet service)-(1,0,0)
• OnlineBackup - is the online backup service activated (Yes, No, No internet service)-(1,0,0)
• DeviceProtection - does the client have equipment insurance (Yes, No, No internet service)-(1,0,0)
• TechSupport - is the technical support service connected (Yes, No, No internet service)-(1,0,0)
• StreamingTV - is the streaming TV service connected (Yes, No, No internet service)-(1,0,0)
• StreamingMovies - is the streaming cinema service activated (Yes, No, No internet service)-(1,0,0)
• Contract - type of customer contract (Month-to-month, One year, Two year) - (month-to-month - 1, One Year - 12, Two Year = 24)
• PaperlessBilling - whether the client uses paperless billing (Yes, No) - (1,0)
• PaymentMethod - payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))
• MonthlyCharges - current monthly payment
• TotalCharges - the total amount that the client paid for the services for the entire time
• Churn - whether there was a churn (Yes or No) - (1,0)

# Feature engineering - convert the object features to integer based on the category
dfa=dfa.replace('Yes',1)
dfa=dfa.replace('No',0)
dfa=dfa.replace('No internet service',0)
dfa=dfa.replace('No phone service',0)
dfa=dfa.replace('Fiber optic',2)
dfa=dfa.replace('DSL',1)
dfa=dfa.replace('Male',1)
dfa=dfa.replace('Female',0)
dfa=pd.get_dummies(data=dfa, columns=['Contract','PaymentMethod'],drop_first=True )

dfa.head()

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	...	StreamingMovies	PaperlessBilling	MonthlyCharges	TotalCharges	Churn	Contract_One year	Contract_Two year	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check
0	0	0	1	0	1	0	0	1	0	1	...	0	1	29.85	29.85	0	0	0	0	1	0
1	1	0	0	0	34	1	0	1	1	0	...	0	0	56.95	1889.50	0	1	0	0	0	1
2	1	0	0	0	2	1	0	1	1	1	...	0	1	53.85	108.15	1	0	0	0	0	1
3	1	0	0	0	45	0	0	1	1	0	...	0	0	42.30	1840.75	0	1	0	0	0	0
4	0	0	0	0	2	1	0	2	0	0	...	0	1	70.70	151.65	1	0	0	0	1	0

5 rows × 23 columns

2F. Split the data into 80% train and 20% test.

# Create the features matrix and target vector
X=dfa.drop(['Churn'], axis=1)
y=dfa['Churn']

# Check for imbalanced dataset by numbers
y.value_counts()

0    5174
1    1869
Name: Churn, dtype: int64

# Check for imbalanced dataset by percentage
1869/5174*100

36.122922303826826

# Split the data for training and testing
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

2G. Normalize/Standardize the data with the best suitable approach.

# Using different scaling methods:
# scaler = RobustScaler()
# scaler = MinMaxScaler()
scaler = StandardScaler()

cols_to_scale = ["MonthlyCharges","TotalCharges","tenure"]

X_train[cols_to_scale] = scaler.fit_transform(X_train[cols_to_scale])
X_test[cols_to_scale] = scaler.transform(X_test[cols_to_scale])

X_train.head()

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	...	StreamingTV	StreamingMovies	PaperlessBilling	MonthlyCharges	TotalCharges	Contract_One year	Contract_Two year	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check
2920	0	0	1	0	1.612532	1	0	1	1	1	...	1	1	0	0.674154	1.704560	0	1	0	0	1
2966	0	1	0	0	-0.747907	0	0	1	0	0	...	1	1	1	-0.614894	-0.711711	0	0	0	1	0
6099	0	0	1	1	1.571835	1	1	0	0	0	...	0	0	0	-1.335097	-0.210251	0	1	0	0	0
5482	1	0	1	1	0.025340	1	0	1	0	0	...	1	1	1	0.301578	0.051750	1	0	0	0	1
2012	0	0	1	0	0.595101	1	1	2	1	1	...	1	0	1	1.128231	0.989863	0	0	0	1	0

5 rows × 22 columns

X_test.head()

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	...	StreamingTV	StreamingMovies	PaperlessBilling	MonthlyCharges	TotalCharges	Contract_One year	Contract_Two year	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check
2200	0	0	0	0	-0.544421	1	0	1	0	1	...	1	0	0	-0.220695	-0.547525	1	0	0	1	0
4627	1	0	0	0	1.124165	1	1	2	1	1	...	1	1	1	1.722025	2.098597	1	0	1	0	0
3225	0	0	0	0	-0.788605	1	0	1	1	0	...	1	1	1	0.236709	-0.601027	0	0	0	1	0
2828	1	0	0	0	-1.276971	1	0	0	0	0	...	0	0	0	-1.476477	-0.999163	0	1	0	0	1
3768	0	0	1	0	0.920679	1	1	2	0	1	...	0	0	1	0.429651	0.872062	0	0	1	0	0

5 rows × 22 columns

print(X_train.shape,X_test.shape)
print(y_train.shape,y_test.shape)

(5634, 22) (1409, 22)
(5634,) (1409,)

3. Model building and Improvement:

3A. Train a model using XGBoost. Also print best performing parameters along with train and test performance.

Base model is created in 3A, and the parameter tuning is done in 3B. Considering both part 3A and 3B here.

# Build the model
model = xgb.XGBClassifier(n_jobs=2, random_state=0, verbosity=0)

# Train the model
model.fit(X_train, y_train)
model_pred = model.predict(X_test)

# Classification Accuracy
print('Accuracy on Training data:',model.score(X_train, y_train))
print('Accuracy on Test data:',model.score(X_test, y_test))

# Classification Report
print("Classification Report:\n",classification_report(y_test, model_pred))

# Confusion Matrix
print("Confusion Matrix Chart:")
cm = confusion_matrix(y_test, model_pred, labels=[0, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["Churn-No","Churn-Yes"]],
                  columns = [i for i in ["Churn-No","Churn-Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True, fmt='g')
plt.show()

Accuracy on Training data: 0.9435569755058573
Accuracy on Test data: 0.7778566359119943
Classification Report:
               precision    recall  f1-score   support

           0       0.83      0.88      0.85      1041
           1       0.59      0.49      0.53       368

    accuracy                           0.78      1409
   macro avg       0.71      0.68      0.69      1409
weighted avg       0.77      0.78      0.77      1409

Confusion Matrix Chart:

# Default Parameters used in the base model:
print(model)

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,
              gamma=0, gpu_id=-1, importance_type=None,
              interaction_constraints='', learning_rate=0.300000012,
              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,
              monotone_constraints='()', n_estimators=100, n_jobs=2,
              num_parallel_tree=1, predictor='auto', random_state=0,
              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
              tree_method='exact', validate_parameters=1, verbosity=0)

3B. Improve performance of the XGBoost as much as possible. Also print best performing parameters along with train and test performance.

Case-1: Using oversampling over complete dataset

print('Before oversampling distribution of target vector:')
print(y.value_counts())

Before oversampling distribution of target vector:
0    5174
1    1869
Name: Churn, dtype: int64

# Using SMOTE
# Create the oversampler. 
smote=SMOTE(random_state=0)
X1, y1=smote.fit_resample(X, y)

# Target vector is balanced after oversampling
print('After oversampling distribution of target vector:')
print(y1.value_counts())

After oversampling distribution of target vector:
0    5174
1    5174
Name: Churn, dtype: int64

# Split X and y into training and test set in 80:20 ratio
X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=0.20, random_state=0)

# Build the model
model = xgb.XGBClassifier(n_jobs=2, random_state=0, verbosity=0)

# Train the model
model.fit(X_train, y_train)
model_pred = model.predict(X_test)

# Classification Accuracy
print('Accuracy on Training data:',model.score(X_train, y_train))
print('Accuracy on Test data:',model.score(X_test, y_test))

# Classification Report
print("Classification Report:\n",classification_report(y_test, model_pred))

# Confusion Matrix
print("Confusion Matrix Chart:")
cm = confusion_matrix(y_test, model_pred, labels=[0, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["Churn-No","Churn-Yes"]],
                  columns = [i for i in ["Churn-No","Churn-Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True, fmt='g')
plt.show()

Accuracy on Training data: 0.9544575984537328
Accuracy on Test data: 0.8396135265700483
Classification Report:
               precision    recall  f1-score   support

           0       0.85      0.82      0.84      1033
           1       0.83      0.86      0.84      1037

    accuracy                           0.84      2070
   macro avg       0.84      0.84      0.84      2070
weighted avg       0.84      0.84      0.84      2070

Confusion Matrix Chart:

Case-2: Using oversampling over training dataset only

# Split X and y into training and test set in 80:20 ratio
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0)

# Using SMOTE
# Create the oversampler.
smote=SMOTE(random_state=0)
X1, y1=smote.fit_resample(X_train, y_train)

# Build the model
model = xgb.XGBClassifier(n_jobs=2, random_state=0, verbosity=0)

# Train the model
model.fit(X1, y1)
model_pred = model.predict(X_test)

# Classification Accuracy
print('Accuracy on Training data:',model.score(X_train, y_train))
print('Accuracy on Test data:',model.score(X_test, y_test))

# Classification Report
print("Classification Report:\n",classification_report(y_test, model_pred))

# Confusion Matrix
print("Confusion Matrix Chart:")
cm = confusion_matrix(y_test, model_pred, labels=[0, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["Churn-No","Churn-Yes"]],
                  columns = [i for i in ["Churn-No","Churn-Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True, fmt='g')
plt.show()

Accuracy on Training data: 0.9391196308129216
Accuracy on Test data: 0.7622427253371186
Classification Report:
               precision    recall  f1-score   support

           0       0.85      0.83      0.84      1041
           1       0.54      0.58      0.56       368

    accuracy                           0.76      1409
   macro avg       0.69      0.70      0.70      1409
weighted avg       0.77      0.76      0.76      1409

Confusion Matrix Chart:

Evaluation metrics allow us to estimate errors to determine how well our models are performing:

Accuracy: ratio of correct predictions over total predictions.

Precision: how often the classifier is correct when it predicts positive.

Recall: how often the classifier is correct for all positive instances.

F-Score: single measurement to combine precision and recall.

Considering the Prediction of customer churn (Recall) as the most important parameter to decide the best model for this problem. We have the highest Recall value here:

• Class 0 predicted correctly for 85% of time. On similar lines for class 1 its 54%.
• Using F1 Score: Precision and Recall is balanced for class 0 by 84% and for class 1 by 56%.
• Precision, Recall, and F1 Score are highest for class 0 followed by class 1.
• We have maximum F1 score for class 0, and minimum for class 1.

Case-3: Using hyperparameter tuning with Oversampling

# Build the model
model = xgb.XGBClassifier(n_jobs=2, random_state=0, verbosity=0)

params = {'max_depth': [3, 5, 6, 10, 15, 20],
          'learning_rate': [0.01, 0.1, 0.2, 0.3],
          'subsample': np.arange(0.5, 1.0, 0.1),
          'colsample_bytree': np.arange(0.4, 1.0, 0.1),
          'colsample_bylevel': np.arange(0.4, 1.0, 0.1),
          'n_estimators': [100, 500, 1000],
          'min_child_weight': [0.5, 1.0, 3.0, 5.0, 7.0, 10.0],
          'gamma':[i/10.0 for i in range(0,5)],
          'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100],
          'reg_lambda': [0.1, 1.0, 5.0, 10.0, 50.0, 100.0]}

gs = RandomizedSearchCV(estimator=model, param_distributions=params, cv=5, verbose=10, n_jobs=2, random_state=0)
gs.fit(X1, y1)

Fitting 5 folds for each of 10 candidates, totalling 50 fits

RandomizedSearchCV(cv=5,
                   estimator=XGBClassifier(base_score=None, booster=None,
                                           colsample_bylevel=None,
                                           colsample_bynode=None,
                                           colsample_bytree=None,
                                           enable_categorical=False, gamma=None,
                                           gpu_id=None, importance_type=None,
                                           interaction_constraints=None,
                                           learning_rate=None,
                                           max_delta_step=None, max_depth=None,
                                           min_child_weight=None, missing=nan,
                                           monotone_constraints...
                                        'colsample_bytree': array([0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),
                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],
                                        'learning_rate': [0.01, 0.1, 0.2, 0.3],
                                        'max_depth': [3, 5, 6, 10, 15, 20],
                                        'min_child_weight': [0.5, 1.0, 3.0, 5.0,
                                                             7.0, 10.0],
                                        'n_estimators': [100, 500, 1000],
                                        'reg_alpha': [1e-05, 0.01, 0.1, 1, 100],
                                        'reg_lambda': [0.1, 1.0, 5.0, 10.0,
                                                       50.0, 100.0],
                                        'subsample': array([0.5, 0.6, 0.7, 0.8, 0.9])},
                   random_state=0, verbose=10)

pd.DataFrame({'param': gs.cv_results_["params"], 
              'score mean': gs.cv_results_["mean_test_score"], 
              'score s.d.': gs.cv_results_["std_test_score"],
             'rank': gs.cv_results_["rank_test_score"]})

	param	score mean	score s.d.	rank
0	{'subsample': 0.8999999999999999, 'reg_lambda'...	0.839235	0.084279	3
1	{'subsample': 0.5, 'reg_lambda': 1.0, 'reg_alp...	0.833188	0.088651	6
2	{'subsample': 0.8999999999999999, 'reg_lambda'...	0.817454	0.052019	8
3	{'subsample': 0.8999999999999999, 'reg_lambda'...	0.834880	0.084013	4
4	{'subsample': 0.5, 'reg_lambda': 10.0, 'reg_al...	0.816365	0.049265	9
5	{'subsample': 0.7999999999999999, 'reg_lambda'...	0.831129	0.078738	7
6	{'subsample': 0.8999999999999999, 'reg_lambda'...	0.839356	0.081076	1
7	{'subsample': 0.8999999999999999, 'reg_lambda'...	0.839354	0.074579	2
8	{'subsample': 0.6, 'reg_lambda': 5.0, 'reg_alp...	0.834275	0.082426	5
9	{'subsample': 0.7999999999999999, 'reg_lambda'...	0.798336	0.030971	10

print('Best Parameters:', gs.best_params_, 'mean score: ', gs.best_score_, sep='\n')

Best Parameters:
{'subsample': 0.8999999999999999, 'reg_lambda': 10.0, 'reg_alpha': 1e-05, 'n_estimators': 100, 'min_child_weight': 1.0, 'max_depth': 20, 'learning_rate': 0.3, 'gamma': 0.2, 'colsample_bytree': 0.8999999999999999, 'colsample_bylevel': 0.7}
mean score: 
0.8393557278510875

# Build the model
model = xgb.XGBClassifier(n_jobs=2, random_state=0, subsample=0.899999999999999, reg_lambda=10.0, reg_alpha=1e-05, n_estimators=100, 
                          min_child_weight=1.0, max_depth=20, learning_rate=0.3, gamma=0.2, 
                          colsample_bytree=0.8999999999999999, colsample_bylevel=0.7, verbosity=0)

# Train the model
model.fit(X1, y1)
model_pred = model.predict(X_test)

# Classification Accuracy
print('Accuracy on Training data:',model.score(X_train, y_train))
print('Accuracy on Test data:',model.score(X_test, y_test))

# Classification Report
print("Classification Report:\n",classification_report(y_test, model_pred))

# Confusion Matrix
print("Confusion Matrix Chart:")
cm = confusion_matrix(y_test, model_pred, labels=[0, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["Churn-No","Churn-Yes"]],
                  columns = [i for i in ["Churn-No","Churn-Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True, fmt='g')
plt.show()

Accuracy on Training data: 0.9799432019879304
Accuracy on Test data: 0.7622427253371186
Classification Report:
               precision    recall  f1-score   support

           0       0.84      0.84      0.84      1041
           1       0.54      0.55      0.55       368

    accuracy                           0.76      1409
   macro avg       0.69      0.69      0.69      1409
weighted avg       0.76      0.76      0.76      1409

Confusion Matrix Chart:

Case-4: Using hyperparameter tuning without oversampling

# Build the model
model = xgb.XGBClassifier(n_jobs=2, random_state=0, verbosity=0)

params = {'max_depth': [3, 5, 6, 10, 15, 20],
          'learning_rate': [0.01, 0.1, 0.2, 0.3],
          'subsample': np.arange(0.5, 1.0, 0.1),
          'colsample_bytree': np.arange(0.4, 1.0, 0.1),
          'colsample_bylevel': np.arange(0.4, 1.0, 0.1),
          'n_estimators': [100, 500, 1000],
          'min_child_weight': [0.5, 1.0, 3.0, 5.0, 7.0, 10.0],
          'gamma':[i/10.0 for i in range(0,5)],
          'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100],
          'reg_lambda': [0.1, 1.0, 5.0, 10.0, 50.0, 100.0]}

gs = RandomizedSearchCV(estimator=model, param_distributions=params, cv=5, verbose=10, n_jobs=2, random_state=0)
gs.fit(X_train, y_train)

Fitting 5 folds for each of 10 candidates, totalling 50 fits

RandomizedSearchCV(cv=5,
                   estimator=XGBClassifier(base_score=None, booster=None,
                                           colsample_bylevel=None,
                                           colsample_bynode=None,
                                           colsample_bytree=None,
                                           enable_categorical=False, gamma=None,
                                           gpu_id=None, importance_type=None,
                                           interaction_constraints=None,
                                           learning_rate=None,
                                           max_delta_step=None, max_depth=None,
                                           min_child_weight=None, missing=nan,
                                           monotone_constraints...
                                        'colsample_bytree': array([0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),
                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],
                                        'learning_rate': [0.01, 0.1, 0.2, 0.3],
                                        'max_depth': [3, 5, 6, 10, 15, 20],
                                        'min_child_weight': [0.5, 1.0, 3.0, 5.0,
                                                             7.0, 10.0],
                                        'n_estimators': [100, 500, 1000],
                                        'reg_alpha': [1e-05, 0.01, 0.1, 1, 100],
                                        'reg_lambda': [0.1, 1.0, 5.0, 10.0,
                                                       50.0, 100.0],
                                        'subsample': array([0.5, 0.6, 0.7, 0.8, 0.9])},
                   random_state=0, verbose=10)

pd.DataFrame({'param': gs.cv_results_["params"], 
              'score mean': gs.cv_results_["mean_test_score"], 
              'score s.d.': gs.cv_results_["std_test_score"],
              'rank': gs.cv_results_["rank_test_score"]})

	param	score mean	score s.d.	rank
0	{'subsample': 0.8999999999999999, 'reg_lambda'...	0.791622	0.009140	4
1	{'subsample': 0.5, 'reg_lambda': 1.0, 'reg_alp...	0.774759	0.008115	10
2	{'subsample': 0.8999999999999999, 'reg_lambda'...	0.805112	0.007498	1
3	{'subsample': 0.8999999999999999, 'reg_lambda'...	0.785410	0.007893	8
4	{'subsample': 0.5, 'reg_lambda': 10.0, 'reg_al...	0.804402	0.008132	2
5	{'subsample': 0.7999999999999999, 'reg_lambda'...	0.782924	0.008297	9
6	{'subsample': 0.8999999999999999, 'reg_lambda'...	0.790202	0.010668	5
7	{'subsample': 0.8999999999999999, 'reg_lambda'...	0.789491	0.015265	6
8	{'subsample': 0.6, 'reg_lambda': 5.0, 'reg_alp...	0.789314	0.006103	7
9	{'subsample': 0.7999999999999999, 'reg_lambda'...	0.796594	0.008824	3

# Print the best parameters
print('Best Parameters:', gs.best_params_, 'mean score: ', gs.best_score_, sep='\n')

Best Parameters:
{'subsample': 0.8999999999999999, 'reg_lambda': 50.0, 'reg_alpha': 0.01, 'n_estimators': 500, 'min_child_weight': 3.0, 'max_depth': 15, 'learning_rate': 0.01, 'gamma': 0.4, 'colsample_bytree': 0.4, 'colsample_bylevel': 0.5}
mean score: 
0.8051118910766097

# Build the model
model = xgb.XGBClassifier(n_jobs=2, random_state=0, subsample=0.899999999999999, reg_lambda=50.0, reg_alpha=0.01, n_estimators=500, 
                          min_child_weight=3.0, max_depth=15, learning_rate=0.01, gamma=0.4, 
                          colsample_bytree=0.4, colsample_bylevel=0.5, verbosity=0)

# Train the model
model.fit(X_train, y_train)
model_pred = model.predict(X_test)

# Plot features importance chart
plot_importance(model)
plt.show()

# Classification Accuracy
print('Accuracy on Training data:',model.score(X_train, y_train))
print('Accuracy on Test data:',model.score(X_test, y_test))

# Classification Report
print("Classification Report:\n",classification_report(y_test, model_pred))

# Confusion Matrix
print("Confusion Matrix Chart:")
cm = confusion_matrix(y_test, model_pred, labels=[0, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["Churn-No","Churn-Yes"]],
                  columns = [i for i in ["Churn-No","Churn-Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True, fmt='g')
plt.show()

Accuracy on Training data: 0.8166489172878949
Accuracy on Test data: 0.7920511000709723
Classification Report:
               precision    recall  f1-score   support

           0       0.82      0.91      0.87      1041
           1       0.65      0.45      0.53       368

    accuracy                           0.79      1409
   macro avg       0.74      0.68      0.70      1409
weighted avg       0.78      0.79      0.78      1409

Confusion Matrix Chart:

Refer the Case-2 for best performing model above. Model tuning will continue in Part-B

Part-B: Solution

• DOMAIN: IT

• CONTEXT: The purpose is to build a machine learning workflow that will work autonomously irrespective of Data and users can save efforts involved in building workflows for each dataset.

• PROJECT OBJECTIVE: Build a machine learning workflow that will run autonomously with the csv file and return best performing model.

• STEPS AND TASK:

1. Build a simple ML workflow which will accept a single ‘.csv’ file as input and return a trained base model that can be used for predictions. You can use 1 Dataset from Part 1 (single/merged).
2. Create separate functions for various purposes.
3. Various base models should be trained to select the best performing model.
4. Pickle file should be saved for the best performing model.

• Include best coding practices in the code:
• Modularization
• Maintainability
• Well commented code etc.

# Import all the relevant libraries needed to complete the analysis, visualization, modeling and presentation
import pandas as pd
import numpy as np
import os

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set_style('darkgrid')
%matplotlib inline

from scipy import stats
from scipy.stats import zscore

from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import RobustScaler
from sklearn import model_selection
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV, StratifiedKFold
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc, roc_auc_score, ConfusionMatrixDisplay, precision_score, recall_score, f1_score, plot_roc_curve, precision_recall_curve, plot_precision_recall_curve, average_precision_score

from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression, RidgeClassifier
from sklearn.naive_bayes import GaussianNB, BernoulliNB
from sklearn.svm import SVC

import xgboost as xgb
from xgboost import plot_importance
from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier

from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import VotingClassifier

from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTENC
from imblearn.over_sampling import SMOTE

import warnings
warnings.filterwarnings("ignore")

1. Import and understand the Data:

# Using the complete dataset from Part-A

# CSV File 1
dfa1=pd.read_csv('TelcomCustomer-Churn_1.csv')
# CSV File 2
dfa2=pd.read_csv('TelcomCustomer-Churn_2.csv')
# customerID is common in both the dataframes
dfa=dfa1.merge(dfa2, left_on='customerID', right_on='customerID')

# CustomerID is the id of the customer with corresponding details. This information may not be requried
# for analysis and modeling as the customerID will be all unique values. So we can drop it safely.
dfa.drop(['customerID'], axis=1, inplace=True)

dfa.info()
dfa.head()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 20 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   gender            7043 non-null   object 
 1   SeniorCitizen     7043 non-null   int64  
 2   Partner           7043 non-null   object 
 3   Dependents        7043 non-null   object 
 4   tenure            7043 non-null   int64  
 5   PhoneService      7043 non-null   object 
 6   MultipleLines     7043 non-null   object 
 7   InternetService   7043 non-null   object 
 8   OnlineSecurity    7043 non-null   object 
 9   OnlineBackup      7043 non-null   object 
 10  DeviceProtection  7043 non-null   object 
 11  TechSupport       7043 non-null   object 
 12  StreamingTV       7043 non-null   object 
 13  StreamingMovies   7043 non-null   object 
 14  Contract          7043 non-null   object 
 15  PaperlessBilling  7043 non-null   object 
 16  PaymentMethod     7043 non-null   object 
 17  MonthlyCharges    7043 non-null   float64
 18  TotalCharges      7043 non-null   object 
 19  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(17)
memory usage: 1.1+ MB

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	Male	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	Male	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

dfa.columns

Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',
       'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',
       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',
       'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod',
       'MonthlyCharges', 'TotalCharges', 'Churn'],
      dtype='object')

2. Automation of Data Pre-processing Task:

Clean Data

Remove the Missing Data

We need to remove data where the value of any feature is nan or na or empty. Imputation is also a great strategy.

def remove_missing(df) : 
  remove = []
  for i, row in df.iterrows():
    if row.isna().values.any() : remove.append(i)
  df.drop(remove,axis=0,inplace=True)

Remove the Mismatch Data

We need to remove data with mismatches: For e.g. a data point with a string value for a numerical feature. For this, we will check what data type is the majority for each feature and remove the data with a different data type for those features.

Provision for exceptions: where we can specify features for which values can have different data types and we do not want to remove mismatches.

def remove_mismatch(df,exceptions=[]) : 
  for col in df : 
    if col in exceptions : continue
    df.reset_index(drop=True, inplace=True)
    s = [False]*len(df[col])
    for i,cell in enumerate(df[col]) : 
      try : n = int(cell)
      except : s[i] = True
    t = s.count(True)
    f = s.count(False)
    st = False
    if(t>f) : st = True
    remove = [i for i in range(len(df[col])) if s[i]!=st]
    df.drop(remove,axis=0,inplace=True)

Convert Numeric Data Stored as String to Numerical Form

Sometimes Numeric Data (e.g. int, float) is stored as a String, this may lead to an error when we train our model or normalize our data. We need to identify such cases and convert them to their original numerical forms.

def str_to_num(df) : 
  for col in df : 
    try : df[col] = pd.to_numeric(df[col])
    except : pass

Single Function to Clean the Data

def clean(df,exceptions_mismatch=[]) : 
  remove_missing(df) 
  remove_mismatch(df,exceptions=exceptions_mismatch)
  str_to_num(df)

# Example
clean(dfa,exceptions_mismatch=['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',
       'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',
       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',
       'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod',
       'MonthlyCharges', 'TotalCharges', 'Churn'])

dfa.info()
dfa.head()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 20 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   gender            7043 non-null   object 
 1   SeniorCitizen     7043 non-null   int64  
 2   Partner           7043 non-null   object 
 3   Dependents        7043 non-null   object 
 4   tenure            7043 non-null   int64  
 5   PhoneService      7043 non-null   object 
 6   MultipleLines     7043 non-null   object 
 7   InternetService   7043 non-null   object 
 8   OnlineSecurity    7043 non-null   object 
 9   OnlineBackup      7043 non-null   object 
 10  DeviceProtection  7043 non-null   object 
 11  TechSupport       7043 non-null   object 
 12  StreamingTV       7043 non-null   object 
 13  StreamingMovies   7043 non-null   object 
 14  Contract          7043 non-null   object 
 15  PaperlessBilling  7043 non-null   object 
 16  PaymentMethod     7043 non-null   object 
 17  MonthlyCharges    7043 non-null   float64
 18  TotalCharges      7043 non-null   object 
 19  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(17)
memory usage: 1.1+ MB

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	Male	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	Male	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

Encode Data

• Label Encoding: Assign an integer to each unique value of a column/feature.
• One Hot Encoding: Convert 1 column to n columns where n is the number of unique values in that column. Each new column represents a unique value in the original column and it contains either 0 or 1. So in each row, only one of the n columns will have the value 1 and the remaining n-1 columns will have the value 0.

We are going to represent the type of encoding we want for each column using a dictionary, where the keys will be the column/feature names and their values will be the type of encoding we want.

# ‘None’ will mean One Hot Encoding
# ‘[]’ would mean Label Encoding without a given order
# ‘[a,b,c…]’ would mean Label Encoding with the list being the order
labels = {}
labels['gender'] = []
labels['SeniorCitizen'] = []
labels['Partner'] = []
labels['Dependents'] = []
# labels['tenure'] = []
labels['PhoneService'] = [] 
labels['MultipleLines'] = [] 
labels['InternetService'] = []
labels['OnlineSecurity'] = [] 
labels['OnlineBackup'] = []
labels['DeviceProtection'] = [] 
labels['TechSupport'] = []
labels['StreamingTV'] = [] 
labels['StreamingMovies'] = [] 
labels['PaperlessBilling'] = []
# labels['MonthlyCharges'] = [] 
# labels['TotalCharges'] = [] 
labels['Churn'] = []
labels['Contract'] = None
labels['PaymentMethod'] = None

# This way experimenting with different encoding will become very easy. 
# For eg., if we want to change the encoding of the ‘Type’ column from One Hot to Label, 
# we can do it by simply changing its value in the labels dictionary from None to [].

Label Encoding

The function takes the column name and order as input.

# Lets say, df['col'] = ['b','a','b','c']

# order = []
 # Label Encoding with no given order
 # df['col'] = [0,1,0,2]
# order = ['a','b','c']
 # Label Encoding with given order
 # df['col'] = [1,0,1,2]
# order = ['a']
 # By giving only a few values in order we can keep remaining values as 'others'
 # df['col'] = [-1,0,-1,-1]

def encode_label(df,col,order=[]) :
  if(order==[]) : order = list(df[col].unique())
  for i,cell in enumerate(df[col]) : 
    try : 
      df.at[i,col] = order.index(df[col][i])
    except : 
      df.at[i,col] = -1

One Hot Encoding

# The function takes the column name as input.

# Lets say, df['col'] = ['b','a','b','c']

# After One Hot Encoding -
 # df['col_b'] = [1,0,1,0]
 # df['col_a'] = [0,1,0,0]
 # df['col_c'] = [0,0,0,1]

def encode_onehot(df,col) :
  k = {}
  n = df[col].shape[0]
  unique = df[col].unique()
  for unq in unique : k[unq] = [0]*n
  for i in range(n) :
    k[df.at[i,col]][i] = 1
  for unq in unique : df[f"{col}_{unq}"] = k[unq] 
  df.drop(col,axis=1,inplace=True)

Single Function For Encoding Data

def encode(df,cols) : 
  for col in cols.keys() : 
    if(cols[col] is None) : encode_onehot(df,col)
    else : encode_label(df,col,cols[col])

# Example
encode(dfa,labels)

dfa.info()
dfa.head()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 25 columns):
 #   Column                                   Non-Null Count  Dtype  
---  ------                                   --------------  -----  
 0   gender                                   7043 non-null   object 
 1   SeniorCitizen                            7043 non-null   int64  
 2   Partner                                  7043 non-null   object 
 3   Dependents                               7043 non-null   object 
 4   tenure                                   7043 non-null   int64  
 5   PhoneService                             7043 non-null   object 
 6   MultipleLines                            7043 non-null   object 
 7   InternetService                          7043 non-null   object 
 8   OnlineSecurity                           7043 non-null   object 
 9   OnlineBackup                             7043 non-null   object 
 10  DeviceProtection                         7043 non-null   object 
 11  TechSupport                              7043 non-null   object 
 12  StreamingTV                              7043 non-null   object 
 13  StreamingMovies                          7043 non-null   object 
 14  PaperlessBilling                         7043 non-null   object 
 15  MonthlyCharges                           7043 non-null   float64
 16  TotalCharges                             7043 non-null   object 
 17  Churn                                    7043 non-null   object 
 18  Contract_Month-to-month                  7043 non-null   int64  
 19  Contract_One year                        7043 non-null   int64  
 20  Contract_Two year                        7043 non-null   int64  
 21  PaymentMethod_Electronic check           7043 non-null   int64  
 22  PaymentMethod_Mailed check               7043 non-null   int64  
 23  PaymentMethod_Bank transfer (automatic)  7043 non-null   int64  
 24  PaymentMethod_Credit card (automatic)    7043 non-null   int64  
dtypes: float64(1), int64(9), object(15)
memory usage: 1.6+ MB

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	...	MonthlyCharges	TotalCharges	Churn	Contract_Month-to-month	Contract_One year	Contract_Two year	PaymentMethod_Electronic check	PaymentMethod_Mailed check	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)
0	0	0	0	0	1	0	0	0	0	0	...	29.85	29.85	0	1	0	0	1	0	0	0
1	1	0	1	0	34	1	1	0	1	1	...	56.95	1889.5	0	0	1	0	0	1	0	0
2	1	0	1	0	2	1	1	0	1	0	...	53.85	108.15	1	1	0	0	0	1	0	0
3	1	0	1	0	45	0	0	0	1	1	...	42.30	1840.75	0	0	1	0	0	0	1	0
4	0	0	1	0	2	1	1	1	0	1	...	70.70	151.65	1	1	0	0	1	0	0	0

5 rows × 25 columns

# Convert the categorical Data type to Numerical for modeling
col=['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService',
    'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
    'StreamingTV', 'StreamingMovies', 'PaperlessBilling', 'TotalCharges', 'Churn']

def convert_to_cont(feature):
    dfa[feature]=pd.to_numeric(dfa[feature], errors='coerce')
    
for c in col:
    convert_to_cont(c)
    
dfa.fillna(dfa.mean(),inplace = True)

dfa.info()
dfa.head()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 25 columns):
 #   Column                                   Non-Null Count  Dtype  
---  ------                                   --------------  -----  
 0   gender                                   7043 non-null   int64  
 1   SeniorCitizen                            7043 non-null   int64  
 2   Partner                                  7043 non-null   int64  
 3   Dependents                               7043 non-null   int64  
 4   tenure                                   7043 non-null   int64  
 5   PhoneService                             7043 non-null   int64  
 6   MultipleLines                            7043 non-null   int64  
 7   InternetService                          7043 non-null   int64  
 8   OnlineSecurity                           7043 non-null   int64  
 9   OnlineBackup                             7043 non-null   int64  
 10  DeviceProtection                         7043 non-null   int64  
 11  TechSupport                              7043 non-null   int64  
 12  StreamingTV                              7043 non-null   int64  
 13  StreamingMovies                          7043 non-null   int64  
 14  PaperlessBilling                         7043 non-null   int64  
 15  MonthlyCharges                           7043 non-null   float64
 16  TotalCharges                             7043 non-null   float64
 17  Churn                                    7043 non-null   int64  
 18  Contract_Month-to-month                  7043 non-null   int64  
 19  Contract_One year                        7043 non-null   int64  
 20  Contract_Two year                        7043 non-null   int64  
 21  PaymentMethod_Electronic check           7043 non-null   int64  
 22  PaymentMethod_Mailed check               7043 non-null   int64  
 23  PaymentMethod_Bank transfer (automatic)  7043 non-null   int64  
 24  PaymentMethod_Credit card (automatic)    7043 non-null   int64  
dtypes: float64(2), int64(23)
memory usage: 1.6 MB

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	...	MonthlyCharges	TotalCharges	Churn	Contract_Month-to-month	Contract_One year	Contract_Two year	PaymentMethod_Electronic check	PaymentMethod_Mailed check	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)
0	0	0	0	0	1	0	0	0	0	0	...	29.85	29.85	0	1	0	0	1	0	0	0
1	1	0	1	0	34	1	1	0	1	1	...	56.95	1889.50	0	0	1	0	0	1	0	0
2	1	0	1	0	2	1	1	0	1	0	...	53.85	108.15	1	1	0	0	0	1	0	0
3	1	0	1	0	45	0	0	0	1	1	...	42.30	1840.75	0	0	1	0	0	0	1	0
4	0	0	1	0	2	1	1	1	0	1	...	70.70	151.65	1	1	0	0	1	0	0	0

5 rows × 25 columns

dfa.columns

Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',
       'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',
       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',
       'StreamingMovies', 'PaperlessBilling', 'MonthlyCharges', 'TotalCharges',
       'Churn', 'Contract_Month-to-month', 'Contract_One year',
       'Contract_Two year', 'PaymentMethod_Electronic check',
       'PaymentMethod_Mailed check', 'PaymentMethod_Bank transfer (automatic)',
       'PaymentMethod_Credit card (automatic)'],
      dtype='object')

Quick EDA

# Correlation of "Churn" with other features
plt.figure(figsize=(15,8))
dfa.corr()['Churn'].sort_values(ascending = False).plot(kind='bar')

<AxesSubplot:>

# Checking Correlation Heatmap
plt.figure(dpi = 540,figsize= (30,25))
mask = np.triu(np.ones_like(dfa.corr()))
sns.heatmap(dfa.corr(),mask = mask, fmt = ".2f",annot=True,lw=1,cmap = 'plasma')
plt.yticks(rotation = 0)
plt.xticks(rotation = 90)
plt.title('Correlation Heatmap')
plt.show()

Normalize Data

• Divide by Largest: Divide all the values in a column by the largest value in that column
• Divide by Constant: Divide all the values in a column by a constant value (e.g. 255 in case of an image)
• Divide by Constant x Largest: Divide all the values in a column by a given constant x largest value in that column
• Min-Max Normalization: Subtracting the minimum value from all the values in a column and then dividing all the values by the largest value in that column (new min will be 0 and new max will be 1)
• Mean Normalization: Subtracting the mean from all the values in a column and then dividing all the values by (largest-smallest).

Different Normalization Functions

# Dividing by largest
def normalize_dbl(df,cols,round=None) : 
  if(type(cols)!=list) : cols = [cols]
  for col in cols : 
    l = df[col].max()
    if round is None : df[col] = df[col].div(l)
    else : df[col] = df[col].div(l).round(round)
        
# Dividing by constant
def normalize_dbc(df,cols,round=None,c=1) :
  if(type(cols)!=list) : cols = [cols]
  for col in cols : 
    if round is None : df[col] = df[col].div(c)
    else : df[col] = df[col].div(c).round(round)
        
# Dividing by constant x largest
def normalize_dblc(df,cols,round=None,c=1) :
  if(type(cols)!=list) : cols = [cols]
  for col in cols : 
    l = df[col].max() * c
    if round is None : df[col] = df[col].div(l)
    else : df[col] = df[col].div(l).round(round)
        
# min-max normalization
def normalize_rescale(df,cols,round=None) :
  if(type(cols)!=list) : cols = [cols]
  for col in cols : 
    df[col] = df[col] - df[col].min()
    l = df[col].max()
    if round is None : df[col] = df[col].div(l)
    else : df[col] = df[col].div(l).round(round)
        
# mean normalization
def normalize_mean(df,cols,round=None) :
  if(type(cols)!=list) : cols = [cols]
  for col in cols : 
    mean = df[col].mean()
    l = df[col].max() - df[col].min()
    df[col] = df[col] - mean
    if round is None : df[col] = df[col].div(l)
    else : df[col] = df[col].div(l).round(round)

Single Function for Normalizing Data

def normalize(df,cols=None,kinds='dbl',round=None,c=1,exceptions=[]) :
  if(cols is None) : 
    cols = []
    for col in df : 
      if(pd.api.types.is_numeric_dtype(df[col])) : 
        if(max(df[col])>1 or min(df[col])<-1) : 
          if(col not in exceptions) : cols.append(col)
  if(type(cols)!=list) : cols = [cols]
  n = len(cols)
  if(type(kinds)!=list) : kinds = [kinds]*n
  for i,kind in enumerate(kinds) : 
    if(kind=='dbl') : normalize_dbl(df,cols[i],round)
    if(kind=='dbc') : normalize_dbc(df,cols[i],round,c)
    if(kind=='dblc') : normalize_dblc(df,cols[i],round,c)
    if(kind in ['min-max','rescale','scale']) : normalize_rescale(df,cols[i],round)
    if(kind=='mean') : normalize_mean(df,cols[i],round)

# Example

# We can vastly vary the overall normalizations by easily making changes in the 
# parameters of this function when we call it. This helps in experimenting with 
# different normalizations.

# Some examples of various ways in which we can normalize our data using this function

# If we want to normalize all columns (it detects numeric columns)
# normalize(df)

# If we want to normalize and round to 3 decimal places –
# normalize(df,round=3)

# If we want to normalize all columns by a kind other than dividing by largest –
# normalize(df,kinds='mean')

# If we want to normalize some columns with a kind and some columns with other kind –
# normalize(df,['Price','Horsepower'],'dbl')
# normalize(df,['AirBags','Cylinders'],'min-max')
# normalize(df,['RPM'],'dblc',c=1.25)

# OR
# normalize(df,['Price','AirBags','Cylinders','Horsepower','RPM'],['dbl','min-max','min-max','dbl','dblc'],c=1.25)

# If we want to normalize all columns except a few
# normalize(df,kinds='min-max',exceptions=['AirBags','RPM'],round=4)

normalize(dfa, kinds='mean', exceptions=['gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',
       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',
       'StreamingMovies', 'PaperlessBilling','Churn', 'Contract_Month-to-month', 
       'Contract_One year','Contract_Two year', 'PaymentMethod_Electronic check',
       'PaymentMethod_Mailed check', 'PaymentMethod_Bank transfer (automatic)',
       'PaymentMethod_Credit card (automatic)'])

dfa.info()
dfa.head()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 25 columns):
 #   Column                                   Non-Null Count  Dtype  
---  ------                                   --------------  -----  
 0   gender                                   7043 non-null   int64  
 1   SeniorCitizen                            7043 non-null   int64  
 2   Partner                                  7043 non-null   int64  
 3   Dependents                               7043 non-null   int64  
 4   tenure                                   7043 non-null   float64
 5   PhoneService                             7043 non-null   int64  
 6   MultipleLines                            7043 non-null   int64  
 7   InternetService                          7043 non-null   int64  
 8   OnlineSecurity                           7043 non-null   int64  
 9   OnlineBackup                             7043 non-null   int64  
 10  DeviceProtection                         7043 non-null   int64  
 11  TechSupport                              7043 non-null   int64  
 12  StreamingTV                              7043 non-null   int64  
 13  StreamingMovies                          7043 non-null   int64  
 14  PaperlessBilling                         7043 non-null   int64  
 15  MonthlyCharges                           7043 non-null   float64
 16  TotalCharges                             7043 non-null   float64
 17  Churn                                    7043 non-null   int64  
 18  Contract_Month-to-month                  7043 non-null   int64  
 19  Contract_One year                        7043 non-null   int64  
 20  Contract_Two year                        7043 non-null   int64  
 21  PaymentMethod_Electronic check           7043 non-null   int64  
 22  PaymentMethod_Mailed check               7043 non-null   int64  
 23  PaymentMethod_Bank transfer (automatic)  7043 non-null   int64  
 24  PaymentMethod_Credit card (automatic)    7043 non-null   int64  
dtypes: float64(3), int64(22)
memory usage: 1.6 MB

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	...	MonthlyCharges	TotalCharges	Churn	Contract_Month-to-month	Contract_One year	Contract_Two year	PaymentMethod_Electronic check	PaymentMethod_Mailed check	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)
0	0	0	0	0	-0.435710	0	0	0	0	0	...	-0.347380	-0.260034	0	1	0	0	1	0	0	0
1	1	0	1	0	0.022623	1	1	0	1	1	...	-0.077728	-0.045442	0	0	1	0	0	1	0	0
2	1	0	1	0	-0.421822	1	1	0	1	0	...	-0.108574	-0.250998	1	1	0	0	0	1	0	0
3	1	0	1	0	0.175401	0	0	0	1	1	...	-0.223499	-0.051067	0	0	1	0	0	0	1	0
4	0	0	1	0	-0.421822	1	1	1	0	1	...	0.059088	-0.245979	1	1	0	0	1	0	0	0

5 rows × 25 columns

Split Data

We will use sklearn and make a function to split data where we won’t even need to mention if we are splitting our data into 2 or 3 portions.

We also need to reset the index of x_train, x_test, etc., otherwise, we can face problems while iterating over them in the future.

from sklearn.model_selection import train_test_split
X = dfa.drop(['Churn'], axis=1)
y = dfa.loc[:,'Churn']

Split-1

def train_test(X,y,train_size=-1,test_size=-1) :

    if(train_size==-1) : train_size = 1-test_size

    X_train,X_test,y_train,y_test = train_test_split(X,y,train_size=train_size,random_state=0)

    X_train.reset_index(drop=True,inplace=True)

    X_test.reset_index(drop=True,inplace=True)

    y_train.reset_index(drop=True,inplace=True)

    y_test.reset_index(drop=True,inplace=True)

    return X_train,X_test,y_train,y_test

Split-2

def train_val_test(X,y,train_size=-1,val_size=-1,test_size=-1) :

    if(train_size==-1) : train_size = 1-val_size-test_size

    if(val_size==-1) : val_size = 1-train_size-test_size

    X_train,X_val,y_train,y_val = train_test_split(X,y,train_size=train_size,random_state=0)

    X_val,X_test,y_val,y_test = train_test_split(X_val,y_val,train_size=(val_size/(1-train_size)),random_state=0)

    X_train.reset_index(drop=True,inplace=True)

    X_val.reset_index(drop=True,inplace=True)

    X_test.reset_index(drop=True,inplace=True)

    y_train.reset_index(drop=True,inplace=True)

    y_val.reset_index(drop=True,inplace=True)

    y_test.reset_index(drop=True,inplace=True)

    return X_train,X_val,X_test,y_train,y_val,y_test

Single Function for Splitting Data

If we pass two sizes in the function (eg. train_size & val_size) then it will be a three-way split, if we pass one size (eg. train_size) it will be a two-way split.

# Single Function
def split(X,y,train_size=-1,val_size=-1,test_size=-1) :
    if(train_size==-1 and val_size==-1) : return train_test(X,y,train_size=1-test_size)
    if(train_size==-1 and test_size==-1) : return train_test(X,y,train_size=1-val_size)
    if(val_size==-1 and test_size==-1) : return train_test(X,y,train_size=train_size)
    return train_val_test(X,y,train_size,val_size,test_size)

# Example for Split-1
# Train Validation Test Split:
X_train,X_val,X_test,y_train,y_val,y_test = split(X,y,train_size=0.7,val_size=0.15)

print(X_train.shape,X_val.shape,X_test.shape)
print(y_train.shape,y_val.shape,y_test.shape)

(4930, 24) (1056, 24) (1057, 24)
(4930,) (1056,) (1057,)

# Example for Split-2
# Train-Test Split:
X_train,X_test,y_train,y_test = split(X,y,train_size=0.80)

print(X_train.shape,X_test.shape)
print(y_train.shape,y_test.shape)

(5634, 24) (1409, 24)
(5634,) (1409,)

Conclusion

We implemented preprocessing functions for Cleaning, Encoding, Normalizing, and Splitting the Data. We saw how organized preprocessing makes our job easier.

After Importing the data, we can pre-process it as per our needs in 4 lines. We can keep modifying the parameters to experiment with different preprocessing approaches.

clean(df,exceptions_mismatch=[])
encode(df,labels)
normalize(df,cols=None,kinds='dbl',round=None,c=1,exceptions=[])
split(X,y,train_size=-1,val_size=-1,test_size=-1)

Refer the above cells for customization of various parameters of these functions.

# Using the dataset from Part-A
# CSV File 1
dfa1=pd.read_csv('TelcomCustomer-Churn_1.csv')
# CSV File 2
dfa2=pd.read_csv('TelcomCustomer-Churn_2.csv')
# customerID is common in both the dataframes
dfa=dfa1.merge(dfa2, left_on='customerID', right_on='customerID')
# We can safely drop the customerID
dfa.drop(['customerID'], axis=1, inplace=True)

# Stacking all the functions for Cleaning, Encoding, Normalizing, and Splitting the Dataframe
# Single Function for Cleaning Data
clean(dfa,exceptions_mismatch=['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',
       'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',
       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',
       'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod',
       'MonthlyCharges', 'TotalCharges', 'Churn'])

# Single Function For Encoding Data
labels = {}
labels['gender'] = []
labels['SeniorCitizen'] = []
labels['Partner'] = []
labels['Dependents'] = []
# labels['tenure'] = []
labels['PhoneService'] = [] 
labels['MultipleLines'] = [] 
labels['InternetService'] = []
labels['OnlineSecurity'] = [] 
labels['OnlineBackup'] = []
labels['DeviceProtection'] = [] 
labels['TechSupport'] = []
labels['StreamingTV'] = [] 
labels['StreamingMovies'] = [] 
labels['PaperlessBilling'] = []
# labels['MonthlyCharges'] = [] 
# labels['TotalCharges'] = [] 
labels['Churn'] = []
labels['Contract'] = None
labels['PaymentMethod'] = None
encode(dfa,labels)

# Convert the categorical Data type to Numerical for modeling
col=['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService',
    'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
    'StreamingTV', 'StreamingMovies', 'PaperlessBilling', 'TotalCharges', 'Churn']
def convert_to_cont(feature):
    dfa[feature]=pd.to_numeric(dfa[feature], errors='coerce')   
for c in col:
    convert_to_cont(c)
dfa.fillna(dfa.mean(),inplace = True)

# Single Function for Normalizing Data
normalize(dfa, kinds='mean', exceptions=['gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',
       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',
       'StreamingMovies', 'PaperlessBilling','Churn', 'Contract_Month-to-month', 
       'Contract_One year','Contract_Two year', 'PaymentMethod_Electronic check',
       'PaymentMethod_Mailed check', 'PaymentMethod_Bank transfer (automatic)',
       'PaymentMethod_Credit card (automatic)'])

# Create the Features Matrix and Target Vector
X = dfa.drop(['Churn'], axis=1)
y = dfa.loc[:,'Churn']

# Single Function for Splitting Data
X_train,X_test,y_train,y_test = split(X,y,train_size=0.80)

print(X_train.shape,X_test.shape)
print(y_train.shape,y_test.shape)

(5634, 24) (1409, 24)
(5634,) (1409,)

X_train.head()

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	...	PaperlessBilling	MonthlyCharges	TotalCharges	Contract_Month-to-month	Contract_One year	Contract_Two year	PaymentMethod_Electronic check	PaymentMethod_Mailed check	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)
0	0	0	0	0	0.550401	1	1	0	1	0	...	1	0.202371	0.446815	0	0	1	0	1	0	0
1	0	1	1	0	-0.255155	0	0	0	0	1	...	0	-0.183201	-0.185853	1	0	0	1	0	0	0
2	0	0	0	1	0.536512	1	2	2	2	2	...	1	-0.398624	-0.054552	0	0	1	0	0	1	0
3	1	0	0	1	0.008734	1	1	0	0	1	...	0	0.090928	0.014049	0	1	0	0	1	0	0
4	0	0	0	0	0.203178	1	2	1	1	0	...	0	0.338192	0.259681	1	0	0	1	0	0	0

5 rows × 24 columns

y_train.head()

0    0
1    0
2    0
3    1
4    1
Name: Churn, dtype: int64

Key Takeaways from the Automation of Data Pre-processing task:

Organized Preprocessing saves time and helps us preprocess different datasets and try different preprocessing without much code change:

• Data Cleaning: We need to remove (or replace) rows with na/nan values, remove rows with the wrong datatype for any feature, and convert numeric data stored as string format in the CSV/excel file back to its original form.
• Data Encoding: We need to encode data as most ML models require numeric data. We implemented label encoding and one hot encoding.
• Data Normalization: It helps in reducing bias towards a feature, and sometimes reduces computation time. We implemented 5 normalization techniques.
• Data Splitting: We need to split our data into the train portion (for fitting the model) and the testing portion (for evaluating the model). Sometimes, we also split into a third portion – validation, which we use to find optimal parameters for our model.

Other than preprocessing too, it’s good to keep our code organized, it helps in making changes later. We should also try to make universal functions taking the dataset as an argument rather than making hardcoded functions that will work only for the dataset we are using at that time.

3. Model Building:

Simple Machine Learning Work flow

Refer section "Conclusion" above to create Preprocessed X_train, X_test, y_train, y_test from a CSV File.

# Build the model
model = xgb.XGBClassifier(n_jobs=2, random_state=0, verbosity=0)

# Train the model
model.fit(X_train, y_train)
model_pred = model.predict(X_test)

# Classification Accuracy
print('')
print('Accuracy on Training data:',model.score(X_train, y_train))
print('Accuracy on Test data:',model.score(X_test, y_test))
print('')

# Classification Report
print("Classification Report:\n",classification_report(y_test, model_pred))

# Confusion Matrix
print("Confusion Matrix Chart:")
cm = confusion_matrix(y_test, model_pred, labels=[0, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["Churn-No","Churn-Yes"]],
                  columns = [i for i in ["Churn-No","Churn-Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True, fmt='g')
plt.show()

print('Default Parameters used in the base model:')
print(model)

Accuracy on Training data: 0.9449769258075967
Accuracy on Test data: 0.7757274662881476

Classification Report:
               precision    recall  f1-score   support

           0       0.83      0.87      0.85      1041
           1       0.58      0.51      0.54       368

    accuracy                           0.78      1409
   macro avg       0.71      0.69      0.70      1409
weighted avg       0.77      0.78      0.77      1409

Confusion Matrix Chart:

Default Parameters used in the base model:
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,
              gamma=0, gpu_id=-1, importance_type=None,
              interaction_constraints='', learning_rate=0.300000012,
              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,
              monotone_constraints='()', n_estimators=100, n_jobs=2,
              num_parallel_tree=1, predictor='auto', random_state=0,
              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
              tree_method='exact', validate_parameters=1, verbosity=0)

Functional Approach: ML work flow template

# ML Workflow to select a best performing model
from numpy import mean
from numpy import std
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import AdaBoostClassifier
from matplotlib import pyplot

# Get the dataset
def get_dataset():
	Xm, ym = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=0)
	return Xm, ym

# Get a list of models to evaluate
def get_models():
	models = dict()
	# define number of trees to consider
	n_trees = [10, 50, 100, 500, 1000, 5000]
	for n in n_trees:
		models[str(n)] = AdaBoostClassifier(random_state=0, n_estimators=n)
	return models

# Evaluate a given model using cross-validation
def evaluate_model(model, Xm, ym):
	# define the evaluation procedure
	cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=0)
	# evaluate the model and collect the results
	scores = cross_val_score(model, Xm, ym, scoring='accuracy', cv=cv, n_jobs=2)
	return scores

# Define dataset
Xm, ym = get_dataset()
# Get the models to evaluate
models = get_models()
# Evaluate the models and store results
results, names = list(), list()
for name, model in models.items():
	# Evaluate the model
	scores = evaluate_model(model, Xm, ym)
	# Store the results
	results.append(scores)
	names.append(name)
	# Summarize the performance along the way
	print('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))
# Plot model performance for comparison
pyplot.boxplot(results, labels=names, showmeans=True)
pyplot.show()

>10 0.689 (0.048)
>50 0.746 (0.047)
>100 0.748 (0.042)
>500 0.734 (0.043)
>1000 0.723 (0.051)
>5000 0.707 (0.049)

Select the best performing model

# Use K-Fold Cross Validation for model selection
# Define various classification models
BaggingClassifier=BaggingClassifier(n_jobs=2, random_state=0)
RandomForest=RandomForestClassifier(n_jobs=2, random_state=0)
AdaBoostClassifier=AdaBoostClassifier(random_state=0)
GBClassifier=GradientBoostingClassifier(random_state=0)
XGBClassifier=xgb.XGBClassifier(n_jobs=2, random_state=0, verbosity = 0)
LGBMClassifier=LGBMClassifier(n_jobs=2, random_state=0)
CatBoostClassifier=CatBoostClassifier(thread_count=2, random_seed=0)

Hybrid = []
Hybrid.append(['RidgeClassifier',RidgeClassifier(random_state=0)])
Hybrid.append(['Logistic Regression',LogisticRegression(n_jobs=2, random_state=0)])
Hybrid.append(['SVM',SVC(random_state=0)])
Hybrid.append(['KNeigbors',KNeighborsClassifier(n_jobs=2)])
Hybrid.append(['GaussianNB',GaussianNB()])
Hybrid.append(['BernoulliNB',BernoulliNB()])
Hybrid.append(['DecisionTree',DecisionTreeClassifier(random_state=0)])

Hybrid_ensemble=VotingClassifier(Hybrid, n_jobs=2)

# K Fold Cross Validation Scores

seed = 0

# Create models
models = []
models.append(('XGBClassifier', XGBClassifier))
models.append(('RandomForest', RandomForest))
models.append(('AdaBoostClassifier', AdaBoostClassifier))
models.append(('GBClassifier', GBClassifier))
models.append(('LGBMClassifier', LGBMClassifier))
models.append(('CatBoostClassifier', CatBoostClassifier))
models.append(('BaggingClassifier', BaggingClassifier))
models.append(('Hybrid_ensemble', Hybrid_ensemble))

# Evaluate each model in turn
results = []
names = []

# Use different metrics based on context
scoring = 'accuracy'
# scoring = 'precision'
# scoring = 'recall'
# scoring = 'f1'
for name, model in models:
	kfold = model_selection.KFold(n_splits=10,random_state=seed,shuffle=True)
	cv_results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring, n_jobs=2)
	results.append(cv_results)
	names.append(name)
	msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
	print(msg)
    
# Boxplot for algorithm comparison
fig = plt.figure(figsize=(12,5))
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()

XGBClassifier: 0.790714 (0.006376)
RandomForest: 0.792561 (0.012951)
AdaBoostClassifier: 0.802217 (0.009586)
GBClassifier: 0.801934 (0.011641)
LGBMClassifier: 0.799233 (0.010807)
CatBoostClassifier: 0.801225 (0.014142)
BaggingClassifier: 0.778360 (0.014293)
Hybrid_ensemble: 0.793697 (0.010147)

# Use SMOTE to handle the imbalanced dataset
# Create the oversampler.
smote=SMOTE(random_state=0)
X1, y1=smote.fit_resample(X_train, y_train)

base_1 = []
for m in range(len(models)):
    base_2 = []
    model = models[m][1]
    model.fit(X1,y1)
    y_pred = model.predict(X_test)
    y1_pred = model.predict(X1)
    cm = confusion_matrix(y_test,y_pred)
    accuracies = cross_val_score(estimator= model, X = X1, y = y1, cv=10)

# k-fOLD Validation
    roc = roc_auc_score(y_test,y_pred)
    precision = precision_score(y_test,y_pred)
    recall = recall_score(y_test,y_pred)
    f1 = f1_score(y_test,y_pred)
    print(models[m][0],':')
    print(cm)
    print('Train Accuracy Score: ',accuracy_score(y1,y1_pred))
    print('Test Accuracy Score: ',accuracy_score(y_test,y_pred))
    print('K-Fold Validation Mean Accuracy: {:.2f} %;'.format(accuracies.mean()*100), 
          'Standard Deviation: {:.2f} %'.format(accuracies.std()*100))
    print('ROC AUC Score: {:.2f} %'.format(roc))
    print('Precision: {:.2f} %'.format(precision))
    print('Recall: {:.2f} %'.format(recall))
    print('F1 Score: {:.2f} %'.format(f1))
    print('Classification Report:')
    print(classification_report(y_test, y_pred)) 
    print('-'*60)
    base_2.append(models[m][0])
    base_2.append(accuracy_score(y1,y1_pred)*100)
    base_2.append(accuracy_score(y_test,y_pred)*100)
    base_2.append(accuracies.mean()*100)
    base_2.append(accuracies.std()*100)
    base_2.append(roc)
    base_2.append(precision)
    base_2.append(recall)
    base_2.append(f1)
    base_1.append(base_2)

XGBClassifier :
[[861 180]
 [139 229]]
Train Accuracy Score:  0.9536656181950157
Test Accuracy Score:  0.7735982966643009
K-Fold Validation Mean Accuracy: 83.54 %; Standard Deviation: 5.09 %
ROC AUC Score: 0.72 %
Precision: 0.56 %
Recall: 0.62 %
F1 Score: 0.59 %
Classification Report:
              precision    recall  f1-score   support

           0       0.86      0.83      0.84      1041
           1       0.56      0.62      0.59       368

    accuracy                           0.77      1409
   macro avg       0.71      0.72      0.72      1409
weighted avg       0.78      0.77      0.78      1409

------------------------------------------------------------
RandomForest :
[[844 197]
 [136 232]]
Train Accuracy Score:  0.9983063150254052
Test Accuracy Score:  0.7636621717530163
K-Fold Validation Mean Accuracy: 85.11 %; Standard Deviation: 3.09 %
ROC AUC Score: 0.72 %
Precision: 0.54 %
Recall: 0.63 %
F1 Score: 0.58 %
Classification Report:
              precision    recall  f1-score   support

           0       0.86      0.81      0.84      1041
           1       0.54      0.63      0.58       368

    accuracy                           0.76      1409
   macro avg       0.70      0.72      0.71      1409
weighted avg       0.78      0.76      0.77      1409

------------------------------------------------------------
AdaBoostClassifier :
[[786 255]
 [ 92 276]]
Train Accuracy Score:  0.8005081054923784
Test Accuracy Score:  0.7537260468417317
K-Fold Validation Mean Accuracy: 79.64 %; Standard Deviation: 1.61 %
ROC AUC Score: 0.75 %
Precision: 0.52 %
Recall: 0.75 %
F1 Score: 0.61 %
Classification Report:
              precision    recall  f1-score   support

           0       0.90      0.76      0.82      1041
           1       0.52      0.75      0.61       368

    accuracy                           0.75      1409
   macro avg       0.71      0.75      0.72      1409
weighted avg       0.80      0.75      0.77      1409

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GBClassifier :
[[799 242]
 [104 264]]
Train Accuracy Score:  0.8263972901040406
Test Accuracy Score:  0.7544357700496807
K-Fold Validation Mean Accuracy: 80.69 %; Standard Deviation: 2.40 %
ROC AUC Score: 0.74 %
Precision: 0.52 %
Recall: 0.72 %
F1 Score: 0.60 %
Classification Report:
              precision    recall  f1-score   support

           0       0.88      0.77      0.82      1041
           1       0.52      0.72      0.60       368

    accuracy                           0.75      1409
   macro avg       0.70      0.74      0.71      1409
weighted avg       0.79      0.75      0.77      1409

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LGBMClassifier :
[[835 206]
 [134 234]]
Train Accuracy Score:  0.8991047665134285
Test Accuracy Score:  0.758694109297374
K-Fold Validation Mean Accuracy: 83.29 %; Standard Deviation: 3.94 %
ROC AUC Score: 0.72 %
Precision: 0.53 %
Recall: 0.64 %
F1 Score: 0.58 %
Classification Report:
              precision    recall  f1-score   support

           0       0.86      0.80      0.83      1041
           1       0.53      0.64      0.58       368

    accuracy                           0.76      1409
   macro avg       0.70      0.72      0.71      1409
weighted avg       0.78      0.76      0.77      1409

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Learning rate set to 0.025387
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987:	learn: 0.2817791	total: 5.48s	remaining: 66.6ms
988:	learn: 0.2816479	total: 5.49s	remaining: 61ms
989:	learn: 0.2815362	total: 5.5s	remaining: 55.5ms
990:	learn: 0.2814508	total: 5.5s	remaining: 50ms
991:	learn: 0.2813381	total: 5.51s	remaining: 44.4ms
992:	learn: 0.2811667	total: 5.52s	remaining: 38.9ms
993:	learn: 0.2810079	total: 5.53s	remaining: 33.4ms
994:	learn: 0.2809462	total: 5.53s	remaining: 27.8ms
995:	learn: 0.2808438	total: 5.54s	remaining: 22.3ms
996:	learn: 0.2807062	total: 5.55s	remaining: 16.7ms
997:	learn: 0.2806417	total: 5.56s	remaining: 11.1ms
998:	learn: 0.2805624	total: 5.57s	remaining: 5.57ms
999:	learn: 0.2805098	total: 5.57s	remaining: 0us
CatBoostClassifier :
[[838 203]
 [129 239]]
Train Accuracy Score:  0.8971691265424631
Test Accuracy Score:  0.7643718949609652
K-Fold Validation Mean Accuracy: 83.60 %; Standard Deviation: 3.79 %
ROC AUC Score: 0.73 %
Precision: 0.54 %
Recall: 0.65 %
F1 Score: 0.59 %
Classification Report:
              precision    recall  f1-score   support

           0       0.87      0.80      0.83      1041
           1       0.54      0.65      0.59       368

    accuracy                           0.76      1409
   macro avg       0.70      0.73      0.71      1409
weighted avg       0.78      0.76      0.77      1409

------------------------------------------------------------
BaggingClassifier :
[[868 173]
 [159 209]]
Train Accuracy Score:  0.9887490926687637
Test Accuracy Score:  0.7643718949609652
K-Fold Validation Mean Accuracy: 82.24 %; Standard Deviation: 4.34 %
ROC AUC Score: 0.70 %
Precision: 0.55 %
Recall: 0.57 %
F1 Score: 0.56 %
Classification Report:
              precision    recall  f1-score   support

           0       0.85      0.83      0.84      1041
           1       0.55      0.57      0.56       368

    accuracy                           0.76      1409
   macro avg       0.70      0.70      0.70      1409
weighted avg       0.77      0.76      0.77      1409

------------------------------------------------------------
Hybrid_ensemble :
[[770 271]
 [ 97 271]]
Train Accuracy Score:  0.8219211226711831
Test Accuracy Score:  0.7388218594748048
K-Fold Validation Mean Accuracy: 80.27 %; Standard Deviation: 2.05 %
ROC AUC Score: 0.74 %
Precision: 0.50 %
Recall: 0.74 %
F1 Score: 0.60 %
Classification Report:
              precision    recall  f1-score   support

           0       0.89      0.74      0.81      1041
           1       0.50      0.74      0.60       368

    accuracy                           0.74      1409
   macro avg       0.69      0.74      0.70      1409
weighted avg       0.79      0.74      0.75      1409

------------------------------------------------------------

model_comparison = pd.DataFrame(base_1,columns=['Model','Train_Accuracy','Test_Accuracy','K-Fold Mean Accuracy',
                                                'Std.Deviation','ROC_AUC','Precision','Recall','F1 Score'])

model_comparison.sort_values(by=['Recall','F1 Score'],inplace=True,ascending=False)
model_comparison

	Model	Train_Accuracy	Test_Accuracy	K-Fold Mean Accuracy	Std.Deviation	ROC_AUC	Precision	Recall	F1 Score
2	AdaBoostClassifier	80.050811	75.372605	79.640054	1.607435	0.752522	0.519774	0.750000	0.614016
7	Hybrid_ensemble	82.192112	73.882186	80.269418	2.047991	0.738043	0.500000	0.736413	0.595604
3	GBClassifier	82.639729	75.443577	80.692825	2.398255	0.742461	0.521739	0.717391	0.604119
5	CatBoostClassifier	89.716913	76.437189	83.596974	3.793297	0.727226	0.540724	0.649457	0.590123
4	LGBMClassifier	89.910477	75.869411	83.294632	3.944543	0.718991	0.531818	0.635870	0.579208
1	RandomForest	99.830632	76.366217	85.108739	3.093462	0.720597	0.540793	0.630435	0.582183
0	XGBClassifier	95.366562	77.359830	83.536851	5.094791	0.724686	0.559902	0.622283	0.589447
6	BaggingClassifier	98.874909	76.437189	82.242330	4.338744	0.700874	0.547120	0.567935	0.557333

Best Model: AdaBoostClassifier based on ROC_AUC, Precision, Recall and F1 Score

1. Prediction of customer churn (Recall) is most important parameter to decide the best model for this problem. AdaBoostClassifier has the highest Recall value here.
2. Both Hybrid_ensemble and GBClassifier are next to the AdaBoostClassifier in performance.
3. XgBoost, CatBoost, and LightGBM are performing on similar scales. A balanced dataset may drastically change their performances.
4. Hyper-parameter tuning of the base model may improve the performance.

Using hyperparameter tuning without oversampling

from sklearn.ensemble import AdaBoostClassifier

# Build the model
model = AdaBoostClassifier(random_state=0)

# Other Parameters to be used:
# 'base_estimator': [RidgeClassifier(random_state=0),LogisticRegression(n_jobs=2, random_state=0),
#                    SVC(random_state=0), GaussianNB(), BernoulliNB(),DecisionTreeClassifier(random_state=0)]
params = {'n_estimators':list(range(2, 102, 2)), 
          'learning_rate':[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
          'algorithm': ['SAMME', 'SAMME.R']}

gs = RandomizedSearchCV(estimator=model, param_distributions=params, cv=5, verbose=10, n_jobs=2, random_state=0)
gs.fit(X_train, y_train)

Fitting 5 folds for each of 10 candidates, totalling 50 fits

RandomizedSearchCV(cv=5, estimator=AdaBoostClassifier(random_state=0), n_jobs=2,
                   param_distributions={'algorithm': ['SAMME', 'SAMME.R'],
                                        'learning_rate': [0.1, 0.2, 0.3, 0.4,
                                                          0.5, 0.6, 0.7, 0.8,
                                                          0.9, 1],
                                        'n_estimators': [2, 4, 6, 8, 10, 12, 14,
                                                         16, 18, 20, 22, 24, 26,
                                                         28, 30, 32, 34, 36, 38,
                                                         40, 42, 44, 46, 48, 50,
                                                         52, 54, 56, 58, 60, ...]},
                   random_state=0, verbose=10)

pd.DataFrame({'param': gs.cv_results_["params"], 
              'score mean': gs.cv_results_["mean_test_score"], 
              'score s.d.': gs.cv_results_["std_test_score"],
              'rank': gs.cv_results_["rank_test_score"]})

	param	score mean	score s.d.	rank
0	{'n_estimators': 70, 'learning_rate': 0.4, 'al...	0.807775	0.007268	1
1	{'n_estimators': 20, 'learning_rate': 0.2, 'al...	0.789671	0.010487	9
2	{'n_estimators': 60, 'learning_rate': 0.3, 'al...	0.803870	0.007200	5
3	{'n_estimators': 86, 'learning_rate': 0.4, 'al...	0.804757	0.006453	3
4	{'n_estimators': 72, 'learning_rate': 0.7, 'al...	0.803692	0.006659	6
5	{'n_estimators': 28, 'learning_rate': 0.6, 'al...	0.800497	0.008962	7
6	{'n_estimators': 16, 'learning_rate': 0.5, 'al...	0.804402	0.011307	4
7	{'n_estimators': 20, 'learning_rate': 0.8, 'al...	0.799788	0.010643	8
8	{'n_estimators': 20, 'learning_rate': 0.1, 'al...	0.733582	0.000344	10
9	{'n_estimators': 48, 'learning_rate': 0.5, 'al...	0.804935	0.008103	2

# Print the best parameters
print('Best Parameters:', gs.best_params_, 'mean score: ', gs.best_score_, sep='\n')

Best Parameters:
{'n_estimators': 70, 'learning_rate': 0.4, 'algorithm': 'SAMME.R'}
mean score: 
0.8077749286447145

# Build the model
model = AdaBoostClassifier(random_state=0, n_estimators=70, learning_rate=0.4, algorithm='SAMME.R')

# Train the model
model.fit(X_train, y_train)
model_pred = model.predict(X_test)

# Classification Accuracy
print('Accuracy on Training data:',model.score(X_train, y_train))
print('Accuracy on Test data:',model.score(X_test, y_test))

# Classification Report
print("Classification Report:\n",classification_report(y_test, model_pred))

# Confusion Matrix
print("Confusion Matrix Chart:")
cm = confusion_matrix(y_test, model_pred, labels=[0, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["Churn-No","Churn-Yes"]],
                  columns = [i for i in ["Churn-No","Churn-Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True, fmt='g')
plt.show()

Accuracy on Training data: 0.8091941782037628
Accuracy on Test data: 0.794889992902768
Classification Report:
               precision    recall  f1-score   support

           0       0.84      0.89      0.87      1041
           1       0.63      0.52      0.57       368

    accuracy                           0.79      1409
   macro avg       0.74      0.71      0.72      1409
weighted avg       0.79      0.79      0.79      1409

Confusion Matrix Chart:

We do not see much improvement in the model here.

Using hyperparameter tuning with Oversampling

# Build the model
model = AdaBoostClassifier(random_state=0)

params = {'n_estimators':list(range(2, 102, 2)), 
          'learning_rate':[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
          'algorithm': ['SAMME', 'SAMME.R']}

gs = RandomizedSearchCV(estimator=model, param_distributions=params, cv=5, verbose=10, n_jobs=2, random_state=0)
gs.fit(X1, y1)

Fitting 5 folds for each of 10 candidates, totalling 50 fits

RandomizedSearchCV(cv=5, estimator=AdaBoostClassifier(random_state=0), n_jobs=2,
                   param_distributions={'algorithm': ['SAMME', 'SAMME.R'],
                                        'learning_rate': [0.1, 0.2, 0.3, 0.4,
                                                          0.5, 0.6, 0.7, 0.8,
                                                          0.9, 1],
                                        'n_estimators': [2, 4, 6, 8, 10, 12, 14,
                                                         16, 18, 20, 22, 24, 26,
                                                         28, 30, 32, 34, 36, 38,
                                                         40, 42, 44, 46, 48, 50,
                                                         52, 54, 56, 58, 60, ...]},
                   random_state=0, verbose=10)

pd.DataFrame({'param': gs.cv_results_["params"], 
              'score mean': gs.cv_results_["mean_test_score"], 
              'score s.d.': gs.cv_results_["std_test_score"],
             'rank': gs.cv_results_["rank_test_score"]})

	param	score mean	score s.d.	rank
0	{'n_estimators': 70, 'learning_rate': 0.4, 'al...	0.791315	0.011328	2
1	{'n_estimators': 20, 'learning_rate': 0.2, 'al...	0.776919	0.009599	9
2	{'n_estimators': 60, 'learning_rate': 0.3, 'al...	0.787444	0.011255	4
3	{'n_estimators': 86, 'learning_rate': 0.4, 'al...	0.783936	0.012594	6
4	{'n_estimators': 72, 'learning_rate': 0.7, 'al...	0.793615	0.015035	1
5	{'n_estimators': 28, 'learning_rate': 0.6, 'al...	0.786356	0.012193	5
6	{'n_estimators': 16, 'learning_rate': 0.5, 'al...	0.781153	0.010815	7
7	{'n_estimators': 20, 'learning_rate': 0.8, 'al...	0.777403	0.009757	8
8	{'n_estimators': 20, 'learning_rate': 0.1, 'al...	0.750426	0.015011	10
9	{'n_estimators': 48, 'learning_rate': 0.5, 'al...	0.791074	0.012627	3

print('Best Parameters:', gs.best_params_, 'mean score: ', gs.best_score_, sep='\n')

Best Parameters:
{'n_estimators': 72, 'learning_rate': 0.7, 'algorithm': 'SAMME.R'}
mean score: 
0.7936147022269429

# Build the model
model = AdaBoostClassifier(random_state=0, n_estimators=72, learning_rate=0.7, algorithm='SAMME.R')

# Train the model
model.fit(X1, y1)
model_pred = model.predict(X_test)

# Classification Accuracy
print('Accuracy on Training data:',model.score(X_train, y_train))
print('Accuracy on Test data:',model.score(X_test, y_test))

# Classification Report
print("Classification Report:\n",classification_report(y_test, model_pred))

# Confusion Matrix
print("Confusion Matrix Chart:")
cm = confusion_matrix(y_test, model_pred, labels=[0, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["Churn-No","Churn-Yes"]],
                  columns = [i for i in ["Churn-No","Churn-Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True, fmt='g')
plt.show()

Accuracy on Training data: 0.7635782747603834
Accuracy on Test data: 0.7508871540099361
Classification Report:
               precision    recall  f1-score   support

           0       0.89      0.75      0.82      1041
           1       0.52      0.75      0.61       368

    accuracy                           0.75      1409
   macro avg       0.71      0.75      0.71      1409
weighted avg       0.80      0.75      0.76      1409

Confusion Matrix Chart:

Evaluation metrics allow us to estimate errors to determine how well our models are performing:

Accuracy: ratio of correct predictions over total predictions.

Precision: how often the classifier is correct when it predicts positive.

Recall: how often the classifier is correct for all positive instances.

F-Score: single measurement to combine precision and recall.

Performance here is almost same as that of the base model in section 1.3.3.

4. Pickle file for the best performing model

# Final model from the above modeling process: Base Model without Hypertuning

# Build the model
model = AdaBoostClassifier(random_state=0)

# Train the model
model.fit(X1, y1)
model_pred = model.predict(X_test)

# Classification Accuracy
print('')
print('Accuracy on Training data:',model.score(X_train, y_train))
print('Accuracy on Test data:',model.score(X_test, y_test))
print('')

# Classification Report
print("Classification Report:\n",classification_report(y_test, model_pred))

# Confusion Matrix
print("Confusion Matrix Chart:")
cm = confusion_matrix(y_test, model_pred, labels=[0, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["Churn-No","Churn-Yes"]],
                  columns = [i for i in ["Churn-No","Churn-Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True, fmt='g')
plt.show()

Accuracy on Training data: 0.763400780972666
Accuracy on Test data: 0.7537260468417317

Classification Report:
               precision    recall  f1-score   support

           0       0.90      0.76      0.82      1041
           1       0.52      0.75      0.61       368

    accuracy                           0.75      1409
   macro avg       0.71      0.75      0.72      1409
weighted avg       0.80      0.75      0.77      1409

Confusion Matrix Chart:

# Import pickle Package
import pickle

# Save the ML model to a file in the current working directory
Pkl_Filename = "Pickle_AdaBoostClassifier_Model.pkl"  

with open(Pkl_Filename, 'wb') as file:  
    pickle.dump(model, file)

# Load the ML Model back from the file
with open(Pkl_Filename, 'rb') as file:  
    Pickle_AdaBoostClassifier_Model = pickle.load(file)
    
Pickle_AdaBoostClassifier_Model

AdaBoostClassifier(random_state=0)

References:

1. Towards Data Science
2. Kaggle. Kaggle Code
3. KdNuggets
4. AnalyticsVidhya
5. Wikipedia
6. Numpy
7. Pandas
8. SciPy
9. MatplotLib
10. Seaborn
11. Python
12. Plotly
13. Bokeh
14. RStudio
15. MiniTab
16. Anaconda